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dc.contributor.advisorMotro, Melih M.en_US
dc.contributor.authorJiwa, Safeeren_US
dc.date.accessioned2020-07-31T18:46:18Z
dc.date.available2020-07-31T18:46:18Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/2144/41354
dc.description.abstractOBJECTIVES: Cephalometric analysis is a tool used in orthodontics for craniofacial growth assessment. Magnitude and direction of mandibular growth pose challenges that may impede successful orthodontic treatment. Accurate growth prediction enables the practitioner to improve diagnostics and orthodontic treatment planning. Deep learning provides a novel method due to its ability to analyze massive quantities of data. We compared the growth prediction capabilities of a novel deep learning algorithm with an industry-standard method. METHODS: Using OrthoDx™, 17 mandibular landmarks were plotted on selected serial cephalograms of 101 growing subjects, obtained from the Forsyth Moorrees Twin Study. The Deep Learning Algorithm (DLA) was trained for a 2-year prediction with 81 subjects. X/Y coordinates of initial and final landmark positions were inputted into a multilayer perceptron that was trained to improve its growth prediction accuracy over several iterations. These parameters were then used on 20 test subjects and compared to the ground truth landmark locations to compute the accuracy. The 20 subjects’ growth was also predicted using Ricketts’s growth prediction (RGP) in Dolphin Imaging™ 11.9 and compared to the ground truth. Mean Absolute Error (MAE) of Ricketts and DLA were then compared to each other, and human landmark detection error used as a clinical reference mean (CRM). RESULTS: The 2-year mandibular growth prediction MAE was 4.21mm for DLA and 3.28mm for RGP. DLA’s error for skeletal landmarks was 2.11x larger than CRM, while RGP was 1.78x larger. For dental landmarks, DLA was 2.79x, and Ricketts was 1.73x larger than CRM. CONCLUSIONS: DLA is currently not on par with RGP for a 2-year growth prediction. However, an increase in data volume and increased training may improve DLA’s prediction accuracy. Regardless, significant future improvements to all growth prediction methods would more accurately assess growth from lateral cephalograms and improve orthodontic diagnoses and treatment plans.en_US
dc.language.isoen_US
dc.subjectDentistryen_US
dc.subjectCephalometryen_US
dc.subjectCraniofacialen_US
dc.subjectDeep learningen_US
dc.subjectGrowth predictionen_US
dc.subjectMandibleen_US
dc.subjectOrthodonticsen_US
dc.titleApplicability of deep learning for mandibular growth predictionen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2020-07-29T16:03:18Z
etd.degree.nameMaster of Science in Designen_US
etd.degree.levelmastersen_US
etd.degree.disciplineOrthodontics and Dentofacial Orthopedicsen_US
etd.degree.grantorBoston Universityen_US
dc.identifier.orcid0000-0003-1758-958X


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